计算机工程

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结合粒子群寻优与遗传重采样的RBPF算法

林海波,柯晶晶,张毅   

  1. (重庆邮电大学 信息无障碍工程研发中心,重庆 400065)
  • 收稿日期:2015-10-21 出版日期:2016-11-15 发布日期:2016-11-15
  • 作者简介:林海波(1965—),男,教授,主研方向为工业机器人、智能控制;柯晶晶,硕士研究生;张毅,教授、博士生导师。
  • 基金项目:
    国家科技部国际合作项目(2010DFA12160);重庆市科技攻关项目(CSTC,2010AA2055)。

Rao-Blackwellized Particle Filter Algorithm Combined Particle Swarm Optimization and Genetic Re-sampling

LIN Haibo,KE Jingjing,ZHANG Yi   

  1. (Engineering Research and Development Center of Information Accessibility,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2015-10-21 Online:2016-11-15 Published:2016-11-15

摘要: 针对Rao-Blackwellized粒子滤波器(RBPF)重采样过程存在粒子衰竭、提议分布精确度不高的问题,提出一种改进的RBPF算法。为提高RBPF算法提议分布精确性,在改进的算法中将机器人里程计信息和激光传感器采集的距离信息进行融合,在算法中引入粒子群寻优策略,通过粒子间能效吸引力来调整采样粒子集,同时对重采样中权值较小的粒子进行遗传变异操作,缓解粒子枯竭现象,提高机器人位姿估计一致性,并维持粒子集的多样性。在基于机器人操作系统和配有URG激光传感器的Pioneer3-DX机器人平台上对改进RBPF算法进行可靠性验证。实验结果表明,改进算法在兼顾粒子集多样性的同时能显著提高机器人位姿估计精确性。

关键词: 同时定位与地图构建, Rao-Blackwellized粒子滤波器, 粒子群寻优, 遗传变异, 机器人操作系统

Abstract: To solve the problem that particle degeneration exists in the re-sampling procedure and the proposed distribution is not accurate for Rao-Blackwellized Particle Filter(RBPF),an improved RBPF algorithm with Particle Swarm Optimization(PSO) Genetic Re-sampling is proposed.In order to improve the accuracy of distribution proposed by RBPF algorithm,the improved algorithm fuses the robot’s odometer information and the distance information collected by laser sensor.The Particle Swarm Optimization(PSO) policy is introduced to adjust particle collection in the sampling by the energy efficiency of particles.Meanwhile,Genetic Variation(GV) is performed on particles with smaller weights in re-sampling to relieve particle depletion,improve the consistency of robot’s pose estimation,and maintain the diversity of particles.The algorithm is verified on the Pioneer3-DX robot which is equipped with a URG laser sensor and based on the Robot Operating System(ROS).Experimental results show that the improved RBPF algorithm can significantly improve the accuracy of robot pose estimation while ensuring the diversity of the particle set.

Key words: Simultaneous Localization and Mapping(SLAM), Rao-Blackwellized Particle Filter(RBPF), Particle Swarm Optimization(PSO), Genetic Variation(GV), Robot Operating System(ROS)

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